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Journal articles on the topic 'Unrolling methods'

Author: Grafiati

Published: 25 May 2024

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1

Lee, Songil, Gyouhyung Kyung, Minjoong Kim, et al. "Shaping Rollable Display Devices: Effects of Gripping Condition, Device Thickness, and Hand Length on Bimanual Perceived Grip Comfort." Human Factors: The Journal of the Human Factors and Ergonomics Society 62, no. 5 (2019): 770–86. http://dx.doi.org/10.1177/0018720819855225.

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Objective To examine the effects of the gripping condition, device thickness, and hand length on bimanual perceived grip comfort associated with unrolling hand-held rollable screens. Background Rollable displays can be rolled and unrolled to change screen size. Although diverse rollable display device concepts have been suggested, little is known regarding ergonomic forms for comfortable screen unrolling. Method Thirty young individuals (10 in each hand-length group) evaluated three rollable display device prototypes in three gripping conditions (no restriction on using side bezels, minimal us

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Song, Heping, Qifeng Ding, Jingyao Gong, Hongying Meng, and Yuping Lai. "SALSA-Net: Explainable Deep Unrolling Networks for Compressed Sensing." Sensors 23, no. 11 (2023): 5142. http://dx.doi.org/10.3390/s23115142.

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Deep unrolling networks (DUNs) have emerged as a promising approach for solving compressed sensing (CS) problems due to their superior explainability, speed, and performance compared to classical deep network models. However, the CS performance in terms of efficiency and accuracy remains a principal challenge for approaching further improvements. In this paper, we propose a novel deep unrolling model, SALSA-Net, to solve the image CS problem. The network architecture of SALSA-Net is inspired by unrolling and truncating the split augmented Lagrangian shrinkage algorithm (SALSA) which is used to

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Yu, Youhao, and Richard M. Dansereau. "MsDC-DEQ-Net: Deep Equilibrium Model (DEQ) with Multiscale Dilated Convolution for Image Compressive Sensing (CS)." IET Signal Processing 2024 (January 18, 2024): 1–12. http://dx.doi.org/10.1049/2024/6666549.

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Compressive sensing (CS) is a technique that enables the recovery of sparse signals using fewer measurements than traditional sampling methods. To address the computational challenges of CS reconstruction, our objective is to develop an interpretable and concise neural network model for reconstructing natural images using CS. We achieve this by mapping one step of the iterative shrinkage thresholding algorithm (ISTA) to a deep network block, representing one iteration of ISTA. To enhance learning ability and incorporate structural diversity, we integrate aggregated residual transformations (Re

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Zhang, Linrui, Qin Zhang, Li Shen, Bo Yuan, Xueqian Wang, and Dacheng Tao. "Evaluating Model-Free Reinforcement Learning toward Safety-Critical Tasks." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 12 (2023): 15313–21. http://dx.doi.org/10.1609/aaai.v37i12.26786.

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Safety comes first in many real-world applications involving autonomous agents. Despite a large number of reinforcement learning (RL) methods focusing on safety-critical tasks, there is still a lack of high-quality evaluation of those algorithms that adheres to safety constraints at each decision step under complex and unknown dynamics. In this paper, we revisit prior work in this scope from the perspective of state-wise safe RL and categorize them as projection-based, recovery-based, and optimization-based approaches, respectively. Furthermore, we propose Unrolling Safety Layer (USL), a joint

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Kamali, Hadi Mardani, and Shaahin Hessabi. "A Fault Tolerant Parallelism Approach for Implementing High-Throughput Pipelined Advanced Encryption Standard." Journal of Circuits, Systems and Computers 25, no. 09 (2016): 1650113. http://dx.doi.org/10.1142/s0218126616501139.

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Advanced Encryption Standard (AES) is the most popular symmetric encryption method, which encrypts streams of data by using symmetric keys. The current preferable AES architectures employ effective methods to achieve two important goals: protection against power analysis attacks and high-throughput. Based on a different architectural point of view, we implement a particular parallel architecture for the latter goal, which is capable of implementing a more efficient pipelining in field-programmable gate array (FPGA). In this regard, all intermediate registers which have a role for unrolling the

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Мельник, Л. М., А. С. Конотоп та О. П. Кизимчук. "ЗАСТОСУВАННЯ ТРАДИЦІЙНИХ НАЦІОНАЛЬНИХ ЕЛЕМЕНТІВ ОЗДОБЛЕННЯ В СУЧАСНОМУ ОДЯЗІ". Art and Design, № 2 (15 червня 2018): 51–58. http://dx.doi.org/10.30857/2617-0272.2018.2.6.

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The purpose of this work is to establish the possibility of the decorating of modern women's clothes with artistic elements and ornaments of traditional national clothes of Ukraine different regions and their subsequent reproduction in knitwear. Methods of literary-analytical review and visual-analytical research are used.The elements of clothing decoration that are characteristic of ethnic Ukrainian clothing have been defined during the research. The structures of knitted fabrics based on on openwork, plated and interlooping with unrolling, which simulate different merezhka of traditional Ukr

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Ye, Yutong, Hongyin Zhu, Chaoying Zhang, and Binghai Wen. "Efficient graphic processing unit implementation of the chemical-potential multiphase lattice Boltzmann method." International Journal of High Performance Computing Applications 35, no. 1 (2020): 78–96. http://dx.doi.org/10.1177/1094342020968272.

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The chemical-potential multiphase lattice Boltzmann method (CP-LBM) has the advantages of satisfying the thermodynamic consistency and Galilean invariance, and it realizes a very large density ratio and easily expresses the surface wettability. Compared with the traditional central difference scheme, the CP-LBM uses the Thomas algorithm to calculate the differences in the multiphase simulations, which significantly improves the calculation accuracy but increases the calculation complexity. In this study, we designed and implemented a parallel algorithm for the chemical-potential model on a gra

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Guo, Yang, Wei Gao, Siwei Ma, and Ge Li. "Accelerating Transform Algorithm Implementation for Efficient Intra Coding of 8K UHD Videos." ACM Transactions on Multimedia Computing, Communications, and Applications 18, no. 4 (2022): 1–20. http://dx.doi.org/10.1145/3507970.

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Real-time ultra-high-definition (UHD) video applications have attracted much attention, where the encoder side urgently demands the high-throughput two-dimensional (2D) transform hardware implementation for the latest video coding standards. This article proposes an effective acceleration method for transform algorithm in UHD intra coding based on the third generation of audio video coding standard (AVS3). First, by conducting detailed statistical analysis, we devise an efficient hardware-friendly transform algorithm that can reduce running cycles and resource consumption remarkably. Second, t

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Aydin, Seda Guzel, and Hasan Şakir Bilge. "FPGA Implementation of Image Registration Using Accelerated CNN." Sensors 23, no. 14 (2023): 6590. http://dx.doi.org/10.3390/s23146590.

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Background: Accurate and fast image registration (IR) is critical during surgical interventions where the ultrasound (US) modality is used for image-guided intervention. Convolutional neural network (CNN)-based IR methods have resulted in applications that respond faster than traditional iterative IR methods. However, general-purpose processors are unable to operate at the maximum speed possible for real-time CNN algorithms. Due to its reconfigurable structure and low power consumption, the field programmable gate array (FPGA) has gained prominence for accelerating the inference phase of CNN a

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Wang, Nan, Xiaoling Zhang, Tianwen Zhang, et al. "A Sparse-Model-Driven Network for Efficient and High-Accuracy InSAR Phase Filtering." Remote Sensing 14, no. 11 (2022): 2614. http://dx.doi.org/10.3390/rs14112614.

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Phase filtering is a vital step for interferometric synthetic aperture radar (InSAR) terrain elevation measurements. Existing phase filtering methods can be divided into two categories: traditional model-based and deep learning (DL)-based. Previous studies have shown that DL-based methods are frequently superior to traditional ones. However, most of the existing DL-based methods are purely data-driven and neglect the filtering model, so that they often need to use a large-scale complex architecture to fit the huge training sets. The issue brings a challenge to improve the accuracy of interfero

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Ariño-Gutierrez, Mayte, Mercedes Molero-Senosiain, Barbara Burgos-Blasco, et al. "Challenges of DMEK Technique with Young Corneal Donors’ Grafts: Surgical Keys for Success—A Pilot Study." Journal of Clinical Medicine 12, no. 19 (2023): 6316. http://dx.doi.org/10.3390/jcm12196316.

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Purpose: To report on the surgical maneuvers recommended for a successful unfolding of very young donors in order to accomplish an uneventful Descemet Membrane Endothelial Keratoplasty (DMEK) surgery. Methods: Five patients (three females and two males, mean age 71.2 ± 6.7 years) with Fuchs endothelial cell dystrophy who underwent DMEK with very young donors (between 20 and 30 years old) were included. The following demographic data were assessed: donor’s age, donor’s endothelial cell density (ECD), preservation time, recipient’s age and sex and unfolding surgical time. Best-corrected visual a

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Liang, Yong, Junwen Tan, Zhisong Xie, Zetao Chen, Daoqian Lin, and Zhenhao Yang. "Research on Convolutional Neural Network Inference Acceleration and Performance Optimization for Edge Intelligence." Sensors 24, no. 1 (2023): 240. http://dx.doi.org/10.3390/s24010240.

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In recent years, edge intelligence (EI) has emerged, combining edge computing with AI, and specifically deep learning, to run AI algorithms directly on edge devices. In practical applications, EI faces challenges related to computational power, power consumption, size, and cost, with the primary challenge being the trade-off between computational power and power consumption. This has rendered traditional computing platforms unsustainable, making heterogeneous parallel computing platforms a crucial pathway for implementing EI. In our research, we leveraged the Xilinx Zynq 7000 heterogeneous com

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Lam, Duc Khai, Cam Vinh Du, and Hoai Luan Pham. "QuantLaneNet: A 640-FPS and 34-GOPS/W FPGA-Based CNN Accelerator for Lane Detection." Sensors 23, no. 15 (2023): 6661. http://dx.doi.org/10.3390/s23156661.

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Lane detection is one of the most fundamental problems in the rapidly developing field of autonomous vehicles. With the dramatic growth of deep learning in recent years, many models have achieved a high accuracy for this task. However, most existing deep-learning methods for lane detection face two main problems. First, most early studies usually follow a segmentation approach, which requires much post-processing to extract the necessary geometric information about the lane lines. Second, many models fail to reach real-time speed due to the high complexity of model architecture. To offer a sol

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Wang, Ling, Hai Zhou, Chunjiang Bian, Kangning Jiang, and Xiaolei Cheng. "Hardware Acceleration and Implementation of YOLOX-s for On-Orbit FPGA." Electronics 11, no. 21 (2022): 3473. http://dx.doi.org/10.3390/electronics11213473.

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The rapid development of remote sensing technology has brought about a sharp increase in the amount of remote sensing image data. However, due to the satellite’s limited hardware resources, space, and power consumption constraints, it is difficult to process massive remote sensing images efficiently and robustly using the traditional remote sensing image processing methods. Additionally, the task of satellite-to-ground target detection has higher requirements for speed and accuracy under the conditions of more and more remote sensing data. To solve these problems, this paper proposes an extrem

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Buglaev, Anatoly M. "Device for Wood-Cutting Tool Hardening." Lesnoy Zhurnal (Forestry Journal), no. 5 (October 15, 2021): 134–41. http://dx.doi.org/10.37482/0536-1036-2021-5-134-141.

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Choosing effective methods and devices for surface hardening of wood-cutting tools is problematic due to the variety of their designs and operating conditions. In this regard, the development of such devices becomes an urgent task. According to the literature, one of the effective methods for increasing the service life of machine parts and tools is electrospark hardening or electrospark alloying. Industrial electrospark installations such as “EFI” (electrophysical measurements) and “Elitron” with manual vibrators are used for electrospark hardening. However, using manual vibrators significant

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Lai, Zeqiang, Kaixuan Wei, Ying Fu, Philipp Härtel, and Felix Heide. "∇-Prox: Differentiable Proximal Algorithm Modeling for Large-Scale Optimization." ACM Transactions on Graphics 42, no. 4 (2023): 1–19. http://dx.doi.org/10.1145/3592144.

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Tasks across diverse application domains can be posed as large-scale optimization problems, these include graphics, vision, machine learning, imaging, health, scheduling, planning, and energy system forecasting. Independently of the application domain, proximal algorithms have emerged as a formal optimization method that successfully solves a wide array of existing problems, often exploiting problem-specific structures in the optimization. Although model-based formal optimization provides a principled approach to problem modeling with convergence guarantees, at first glance, this seems to be a

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Popov, Valery A., and Valentin V. Elantsev. "On the improvement efficiency and safety of operation of underground tunnel escalators. Adaptation of processes of planning and control." Izvestiya MGTU MAMI 17, no. 3 (2023): 305–19. http://dx.doi.org/10.17816/2074-0530-492274.

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BACKGROUND: The pace of transport system development not corresponding to current demands of a town is a significant obstacle on the way of achieving target values of business activity and quality of life. One of the main indicators of quality of life in a modern large city is satisfaction with city public transport. The backbone mean of public transport in Saint Petersburg is underground which efficiency indicators are share of accomplishment of train schedule and accomplishment of stations’ operation schedule. However, funding based on subsidies and requirement of proper fulfillment of given

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Park, BoSun, and Seog Chung Seo. "Efficient Implementation of NIST LWC ESTATE Algorithm Using OpenCL and Web Assembly for Secure Communication in Edge Computing Environment." Sensors 21, no. 6 (2021): 1987. http://dx.doi.org/10.3390/s21061987.

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In edge computing service, edge devices collect data from a number of embedded devices, like sensors, CCTVs (Closed-circuit Television), and so on, and communicate with application servers. Since a large portion of communication in edge computing services are conducted in wireless, the transmitted data needs to be properly encrypted. Furthermore, the application servers (resp. edge devices) are responsible for encrypting or decrypting a large amount of data from edge devices (resp. terminal devices), the cryptographic operation needs to be optimized on both server side and edge device side. Ac

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Liu, Shuangli, Pengcheng Wan, and Xin Shang. "HASR-TAI: Hybrid model-based interpretable network and super-resolution network for thermoacoustic imaging." Applied Physics Letters 123, no. 13 (2023). http://dx.doi.org/10.1063/5.0169109.

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Microwave induced thermoacoustic tomography has shown promise for noninvasive and non-ionizing early tumor detection. Nowadays, thermoacoustic reconstruction methods based on deep learning have achieved good and time-efficient results. However, both deep learning methods based on the initial thermoacoustic image and end-to-end methods lack interpretability due to the black-box property of neural networks. In this Letter, we propose an interpretable end-to-end network structure comprising an unrolling part and a super-resolution part. In the unrolling part, a deep unfolding network interprets t

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Shi, Baoshun, Ke Jiang, Shaolei Zhang, Qiusheng Lian, Yanwei Qin, and Yunsong Zhao. "Mud-Net: Multi-domain deep unrolling network for simultaneous sparse-view and metal artifact reduction in computed tomography." Machine Learning: Science and Technology, January 5, 2024. http://dx.doi.org/10.1088/2632-2153/ad1b8e.

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Abstract Sparse-view computed tomography (SVCT) is regarded as a promising technique to accelerate data acquisition and reduce radiation dose. However, in the presence of metallic implants, SVCT inevitably makes the reconstructed CT images suffer from severe metal artifacts and streaking artifacts due to the lack of sufficient projection data. Previous stand-alone SVCT and metal artifact reduction (MAR) methods to solve the problem of simultaneously sparse-view and metal artifact reduction (SVMAR) are plagued by insufficient correction accuracy. To overcome this limitation, we propose a multi-

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Solod, Panadda, Nattha Jindapetch, Kiattisak Sengchuai, et al. "High Level Synthesis Optimizations of Road Lane Detection Development on Zynq-7000." Pertanika Journal of Science and Technology 29, no. 2 (2021). http://dx.doi.org/10.47836/pjst.29.2.01.

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In this work, we proposed High-Level Synthesis (HLS) optimization processes to improve the speed and the resource usage of complex algorithms, especially nested-loop. The proposed HLS optimization processes are divided into four steps: array sizing is performed to decrease the resource usage on Programmable Logic (PL) part, loop analysis is performed to determine which loop must be loop unrolling or loop pipelining, array partitioning is performed to resolve the bottleneck of loop unrolling and loop pipelining, and HLS interface is performed to select the best block level and port level interf

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Je, Changsoo, and Hyung-Min Park. "Binary Stripe Unwrapping Based on Mean-Speed Walk and Local Median Correction for Rapid High-Resolution Structured-Light Range Imaging." International Journal of Sensors, Wireless Communications and Control 13 (December 19, 2022). http://dx.doi.org/10.2174/2210327913666221219091440.

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Aim: Structured light is frequently selected for efficient and accurate depth imaging, and single-frame-based methods have been presented for real-time sensing or imaging dynamic objects. However, many existing single-frame-based methods do not provide sufficient range resolution. Even those capable of sufficient range resolution mostly result in insufficient signal-to-noise ratio or depend on spatially windowed uniqueness, where a larger window makes the identification trickier. Method: This paper presents a novel method for rapid structured-light range sensing using a binary color stripe pat

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Zhou, Qingping, Jaiyu Qian, Junqi Tang, and Jinglai Li. "Deep unrolling networks with recurrent momentum acceleration for nonlinear inverse problems." Inverse Problems, March 20, 2024. http://dx.doi.org/10.1088/1361-6420/ad35e3.

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Abstract Combining the strengths of model-based iterative algorithms and data-driven deep learning solutions, deep unrolling networks (DuNets) have become a popular tool to solve inverse imaging problems. While DuNets have been successfully applied to many linear inverse problems, nonlinear problems tend to impair the performance of the method. Inspired by momentum acceleration techniques that are often used in optimization algorithms, we propose a recurrent momentum acceleration (RMA) framework that uses a long short-term memory recurrent neural network (LSTM-RNN) to simulate the momentum acc

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Li, Tianao, and E. Alexander. "Galaxy image deconvolution for weak gravitational lensing with unrolled plug-and-play ADMM." Monthly Notices of the Royal Astronomical Society: Letters, March 10, 2023. http://dx.doi.org/10.1093/mnrasl/slad032.

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Abstract Removing optical and atmospheric blur from galaxy images significantly improves galaxy shape measurements for weak gravitational lensing and galaxy evolution studies. This ill-posed linear inverse problem is usually solved with deconvolution algorithms enhanced by regularisation priors or deep learning. We introduce a so-called ”physics-informed deep learning” approach to the Point Spread Function (PSF) deconvolution problem in galaxy surveys. We apply algorithm unrolling and the Plug-and-Play technique to the Alternating Direction Method of Multipliers (ADMM), in which a neural netwo

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Gu, Hongyi, Burhaneddin Yaman, Steen Moeller, Jutta Ellermann, Kamil Ugurbil та Mehmet Akçakaya. "Revisiting ℓ1-wavelet compressed-sensing MRI in the era of deep learning". Proceedings of the National Academy of Sciences 119, № 33 (2022). http://dx.doi.org/10.1073/pnas.2201062119.

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Following their success in numerous imaging and computer vision applications, deep-learning (DL) techniques have emerged as one of the most prominent strategies for accelerated MRI reconstruction. These methods have been shown to outperform conventional regularized methods based on compressed sensing (CS). However, in most comparisons, CS is implemented with two or three hand-tuned parameters, while DL methods enjoy a plethora of advanced data science tools. In this work, we revisit ℓ 1 -wavelet CS reconstruction using these modern tools. Using ideas such as algorithm unrolling and advanced op

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Szécsi, Péter György, Gábor Horváth, and Zoltán Porkoláb. "Improved Loop Execution Modeling in the Clang Static Analyzer." Acta Cybernetica, October 22, 2020. http://dx.doi.org/10.14232/actacyb.283176.

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The LLVM Clang Static Analyzer is a source code analysis tool which aims to find bugs in C, C++, and Objective-C programs using symbolic execution, i.e. it simulates the possible execution paths of the code. Currently the simulation of the loops is somewhat naive (but efficient), unrolling the loops a predefined constant number of times. However, this approach can result in a loss of coverage in various cases. This study aims to introduce two alternative approaches which can extend the current method and can be applied simultaneously: (1) determining loops worth to fully unroll with applied he

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Moos, Thorben. "Unrolled Cryptography on Silicon." IACR Transactions on Cryptographic Hardware and Embedded Systems, August 26, 2020, 416–42. http://dx.doi.org/10.46586/tches.v2020.i4.416-442.

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Cryptographic primitives with low-latency performance have gained momentum lately due to an increased demand for real-time applications. Block ciphers such as PRINCE enable data encryption (resp. decryption) within a single clock cycle at a moderately high operating frequency when implemented in a fully-unrolled fashion. Unsurprisingly, many typical environments for unrolled ciphers require protection against physical adversaries as well. Yet, recent works suggest that most common SCA countermeasures are hard to apply to low-latency circuits. Hardware masking, for example, requires register st

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Rajmohan, Shathanaa, N. Ramasubramanian, and Nagi Naganathan. "Design Space Exploration for Reducing Cost of Hardware Trojan Detection and Isolation during Architectural Synthesis." Journal of Circuits, Systems and Computers, December 29, 2020, 2150156. http://dx.doi.org/10.1142/s0218126621501565.

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In past years, software used to be the main concern of computer security, and the hardware was assumed to be safe. However, Hardware Trojans, which are a malicious alteration to the circuit, pose a threat to the security of a system. Trojans may be distributed across different components of the system and can bring down the security by communicating with each other. Redundancy and vendor diversity-based methods exist to detect Hardware Trojans, but with an increase in the hardware overhead. This work proposes a novel vendor allocation procedure to reduce the hardware cost that comes with Troja

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Zhu, Shuping, Wei Gao, and Xiaolei Li. "SSANet: normal-mode interference spectrum extraction via SSA algorithm-unrolled neural network." Frontiers in Marine Science 10 (February 1, 2024). http://dx.doi.org/10.3389/fmars.2023.1342090.

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In ocean acoustic fields, extracting the normal-mode interference spectrum (NMIS) from the received sound intensity spectrum (SIS) plays an important role in waveguide-invariant estimation and underwater source ranging. However, the received SIS often has a low signal-to-noise ratio (SNR) owing to ocean ambient noise and the limitations of the received equipment. This can lead to significant performance degradation for the traditional methods of extracting NMIS at low SNR conditions. To address this issue, a new deep neural network model called SSANet is proposed to obtain NMIS based on unroll

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Peng, Guan‐Ju. "Learning the sparse prior: Modern approaches." WIREs Computational Statistics 16, no. 1 (2024). http://dx.doi.org/10.1002/wics.1646.

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AbstractThe sparse prior has been widely adopted to establish data models for numerous applications. In this context, most of them are based on one of three foundational paradigms: the conventional sparse representation, the convolutional sparse representation, and the multi‐layer convolutional sparse representation. When the data morphology has been adequately addressed, a sparse representation can be obtained by solving the sparse coding problem specified by the data model. This article presents a comprehensive overview of these three models and their corresponding sparse coding problems and

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Heaton, Howard, Samy Wu Fung, Aviv Gibali, and Wotao Yin. "Feasibility-based fixed point networks." Fixed Point Theory and Algorithms for Sciences and Engineering 2021, no. 1 (2021). http://dx.doi.org/10.1186/s13663-021-00706-3.

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AbstractInverse problems consist of recovering a signal from a collection of noisy measurements. These problems can often be cast as feasibility problems; however, additional regularization is typically necessary to ensure accurate and stable recovery with respect to data perturbations. Hand-chosen analytic regularization can yield desirable theoretical guarantees, but such approaches have limited effectiveness recovering signals due to their inability to leverage large amounts of available data. To this end, this work fuses data-driven regularization and convex feasibility in a theoretically

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Alcibahy, Yasmine, Nicola G. Ghazi, Arif O. Khan, and Aniruddha Agarwal. "Surgical repair of macular fold in X-linked retinoschisis initially misdiagnosed as familial exudative vitreoretinopathy." RETINAL Cases & Brief Reports, May 7, 2024. http://dx.doi.org/10.1097/icb.0000000000001600.

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Purpose: To describe the presentation and surgical management of a young boy initially thought to have familial exudative vitreoretinopathy who was ultimately diagnosed with an unusually aggressive form of X-linked retinoschisis that included rapidly progressive bullous retinoschisis and tractional macular fold. Methods: Retrospective case report Results: A 19-month-old boy with straightening of major arcades, peripheral retinal ischemia, and in the left eye, a large macular fold was initially diagnosed as familial exudative vitreoretinopathy. During follow-up, he developed a rapidly progressi

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Chen, Hongling, Mauricio Sacchi, Hojjat Haghshenas Lari, Jinghuai Gao, and Xiudi Jiang. "Nonstationary Seismic Reflectivity Inversion Based on Prior-engaged Semi-supervised Deep Learning Method." GEOPHYSICS, September 23, 2022, 1–72. http://dx.doi.org/10.1190/geo2022-0057.1.

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Reflectivity inversion methods based on a stationary convolution model are essential for seismic data processing. They compress the seismic wavelet, and by broadening the bandwidth of seismic data, they assist the interpretation of seismic sections. Unfortunately, they do not apply to realistic nonstationary deconvolution cases where the seismic wavelet varies as it propagates in the subsurface. Deep learning techniques have been proposed to solve inverse problems where networks can behave as the regularizer of the inverse problem. Our goal is to adopt a semi-supervised deep learning approach

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Waheed Muhammad SANYA, Gaurav BAJPAI, and Haji Ali HAJI. "Implementation and Optimization of Image Processing on the Map of SABRE i.MX_6." International Journal of Scientific Research in Computer Science, Engineering and Information Technology, December 15, 2021, 402–17. http://dx.doi.org/10.32628/cseit217690.

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Vision relieves humans to understand the environmental deviations over a period. These deviations are seen by capturing the images. The digital image plays a dynamic role in everyday life. One of the processes of optimizing the details of an image whilst removing the random noise is image denoising. It is a well-explored research topic in the field of image processing. In the past, the progress made in image denoising has advanced from the improved modeling of digital images. Hence, the major challenges of the image process denoising algorithm is to advance the visual appearance whilst preserv

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